Measuring Contagion with a Bayesian, Time-Varying Coefficient Model
45 Pages Posted: 26 Jan 2004
There are 3 versions of this paper
Measuring Contagion with a Bayesian, Time-Varying Coefficient Model
Measuring Contagion with a Bayesian Time-Varying Coefficient Model
Measuring Contagion and Interdependence with a Bayesian Time-Varying Coefficient Model: An Application to the Chilean Fx Market During the Argentine Crisis
Date Written: September 2003
Abstract
To measure contagion empirically, we propose using a Bayesian time-varying coefficient model estimated with Markov Chain Monte Carlo methods. The proposed measure works in the joint presence of heteroskedasticity and omitted variables and does not require knowledge of the timing of the crisis. It distinguishes contagion not only from interdependence but also from structural breaks. It can be used to investigate positive as well as negative contagion. The proposed measure appears to work well using both simulated and actual data.
Keywords: Contagion, Gibbs sampling, Heteroskedasticity, Omitted variable bias, Time-varying coefficient models
JEL Classification: C11, C15, F41, F42, G15
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Recommended Papers
-
No Contagion, Only Interdependence: Measuring Stock Market Co-Movements
By Kristin J. Forbes and Roberto Rigobon
-
Transmission of Volatility between Stock Markets
By Mervyn King and Sushil Wadhwani
-
Asymmetric Correlations of Equity Portfolios
By Joseph Chen and Andrew Ang
-
Correlations in Price Changes and Volatility Across International Stock Markets
By Yasushi Hamao, Ronald W. Masulis, ...
-
Volatiltiy and Links between National Stock Markets
By Mervyn King, Enrique Sentana, ...
-
A New Approach to Measuring Financial Contagion
By Kee-hong Bae, George Andrew Karolyi, ...
-
Why Do Markets Move Together? An Investigation of U.S.-Japan Stock Return Comovements Using Adrs
-
A New Approach to Measuring Financial Contagion
By Kee-hong Bae, George Andrew Karolyi, ...
-
By Wenling Lin, Robert F. Engle, ...